{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:XMQYNSULAJLEZI6V42AGNIR2SI","short_pith_number":"pith:XMQYNSUL","schema_version":"1.0","canonical_sha256":"bb2186ca8b02564ca3d5e68066a23a9232c0b3b2961de0fd5229d68ce7b4a40d","source":{"kind":"arxiv","id":"2208.02921","version":1},"attestation_state":"computed","paper":{"title":"A flexible, random histogram kernel for discrete-time Hawkes processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"Judith Rousseau, Kerrie Mengersen, Raiha Browning","submitted_at":"2022-08-04T22:49:54Z","abstract_excerpt":"Hawkes processes are a self-exciting stochastic process used to describe phenomena whereby past events increase the probability of the occurrence of future events. This work presents a flexible approach for modelling a variant of these, namely discrete-time Hawkes processes. Most standard models of Hawkes processes rely on a parametric form for the function describing the influence of past events, referred to as the triggering kernel. This is likely to be insufficient to capture the true excitation pattern, particularly for complex data. By utilising trans-dimensional Markov chain Monte Carlo "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2208.02921","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-08-04T22:49:54Z","cross_cats_sorted":["stat.AP","stat.CO"],"title_canon_sha256":"fa23b368d445265e3ea4e507970a40d048ad91a76e3e9f1e6fd6166827b777a1","abstract_canon_sha256":"8036d79d28089db1dbbe8036f83a99c2424ab6e83a67ea646726e190dc38ef47"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:46:17.651088Z","signature_b64":"RLklAUkZW0S+qTPKJkFmCgt0qEB186geNoFcgvS+wNJ7VOvemah6IDekDONeaaiIKWEfnl+ylNzG1VnzhGZCAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb2186ca8b02564ca3d5e68066a23a9232c0b3b2961de0fd5229d68ce7b4a40d","last_reissued_at":"2026-07-05T04:46:17.650608Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:46:17.650608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A flexible, random histogram kernel for discrete-time Hawkes processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"Judith Rousseau, Kerrie Mengersen, Raiha Browning","submitted_at":"2022-08-04T22:49:54Z","abstract_excerpt":"Hawkes processes are a self-exciting stochastic process used to describe phenomena whereby past events increase the probability of the occurrence of future events. This work presents a flexible approach for modelling a variant of these, namely discrete-time Hawkes processes. Most standard models of Hawkes processes rely on a parametric form for the function describing the influence of past events, referred to as the triggering kernel. This is likely to be insufficient to capture the true excitation pattern, particularly for complex data. By utilising trans-dimensional Markov chain Monte Carlo "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.02921","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.02921/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2208.02921","created_at":"2026-07-05T04:46:17.650674+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.02921v1","created_at":"2026-07-05T04:46:17.650674+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.02921","created_at":"2026-07-05T04:46:17.650674+00:00"},{"alias_kind":"pith_short_12","alias_value":"XMQYNSULAJLE","created_at":"2026-07-05T04:46:17.650674+00:00"},{"alias_kind":"pith_short_16","alias_value":"XMQYNSULAJLEZI6V","created_at":"2026-07-05T04:46:17.650674+00:00"},{"alias_kind":"pith_short_8","alias_value":"XMQYNSUL","created_at":"2026-07-05T04:46:17.650674+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XMQYNSULAJLEZI6V42AGNIR2SI","json":"https://pith.science/pith/XMQYNSULAJLEZI6V42AGNIR2SI.json","graph_json":"https://pith.science/api/pith-number/XMQYNSULAJLEZI6V42AGNIR2SI/graph.json","events_json":"https://pith.science/api/pith-number/XMQYNSULAJLEZI6V42AGNIR2SI/events.json","paper":"https://pith.science/paper/XMQYNSUL"},"agent_actions":{"view_html":"https://pith.science/pith/XMQYNSULAJLEZI6V42AGNIR2SI","download_json":"https://pith.science/pith/XMQYNSULAJLEZI6V42AGNIR2SI.json","view_paper":"https://pith.science/paper/XMQYNSUL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.02921&json=true","fetch_graph":"https://pith.science/api/pith-number/XMQYNSULAJLEZI6V42AGNIR2SI/graph.json","fetch_events":"https://pith.science/api/pith-number/XMQYNSULAJLEZI6V42AGNIR2SI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XMQYNSULAJLEZI6V42AGNIR2SI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XMQYNSULAJLEZI6V42AGNIR2SI/action/storage_attestation","attest_author":"https://pith.science/pith/XMQYNSULAJLEZI6V42AGNIR2SI/action/author_attestation","sign_citation":"https://pith.science/pith/XMQYNSULAJLEZI6V42AGNIR2SI/action/citation_signature","submit_replication":"https://pith.science/pith/XMQYNSULAJLEZI6V42AGNIR2SI/action/replication_record"}},"created_at":"2026-07-05T04:46:17.650674+00:00","updated_at":"2026-07-05T04:46:17.650674+00:00"}